A Probabilistic Estimation of the Basic Reproduction Number: A Case of Control Strategy of Pneumonia

Abstract

Deterministic models have been used in the past to understand the epidemiology of infectious diseases, most importantly to estimate the basic reproduction number, R o by using disease parameters. However, the approach overlooks variation on the disease parameter(s) which are function of R o and can introduce random effect on R o. In this paper, we estimate the R o as a random variable by first developing and analyzing a deterministic model for transmission patterns of pneumonia, and then compute the probability distribution of R o using Monte Carlo Markov Chain (MCMC) simulation approach. A detailed analysis of the simulated transmission data, leads to probability distribution of R o as opposed to a single value in the convectional deterministic modeling approach. Results indicate that there is sufficient information generated when uncertainty is considered in the computation of R o and can be used to describe the effect of parameter change in deterministic models.